Code: CS5101  Category: PMP  Credits: 0032
Prerequisite: corequisite for CS5512
Course Content

Introduction to NumPy Regression: linear regression, ridge regression using scipy (3 hours)

Introduction to Matplotlib (6 hours)

Gradient descent method for optimization (3 hours)

Various classification methods using scikitlearn (3 hours)

Principal component analysis, Canonical correlation analysis (6 hours)

Ensemble methods: boosting, bagging, random forests. (3 hours)

Clustering using scikitlearn (6 hours)

Sequential Learning : hidden Markov model (3 hours)

Feed forward NN : Tensorflow (6 hours)
Learning Outcomes
 Given a task, derive a learning model by defining appropriate loss function, regulariser, optimization problem and stating the best possible solution.
 Analyse and compare models and algorithms with respect to their complexity, performance and applicability
 Develop models/algorithms with small modifications of existing standard techniques for a modification of known task
Learning Objectives
 To introduce classical and foundational concepts, results, methodologies and applications in machine learning
 To develop abilities for developing a solution for a given problem starting from problem and data to presenting results
Text Books
 Richard Duda, Peter Hart, David Stork, Pattern Classification, 2nd Ed, John Wiley & Sons, 2001. ISBN 9788126511167
 Christopher Bishop. Pattern Recognition and Machine Learning. ISBN 0387310738.
 Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. ISBN 0387952845.
References
 Tom Mitchell. Machine Learning. McGrawHill. ISBN 0070428077.
 Shai ShalevShwartz, and Shai BenDavid, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014. ISBN 9781107057135.
Past Offerings
 Offered in JulDec, 2021 by Sahely
 Offered in JulDec, 2020 by Sahely
Course Metadata
Item  Details 

Course Title  Machine Learning Lab 
Course Code  CS5101 
Course Credits  0032 
Course Category  PMP 
Approved on  Senate of IIT Palakkad 